About
Contact & Profiles
A5090827745
Research Areas
- Combustion and Detonation Processes
- Fire dynamics and safety research
- Advanced Machining and Optimization Techniques
- Risk and Safety Analysis
- Industrial Vision Systems and Defect Detection
- Advanced machining processes and optimization
North Carolina State University
2021
Abstract Data-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance adaptability, flexibility, prediction performance, horizon online monitoring prediction. This paper proposes use a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try captures long-term dependencies than regular method modeling sequential data, also...
10.1007/s42452-021-04427-5
article
EN
cc-by
SN Applied Sciences
2021-03-09
10.1016/j.fuel.2025.134369
article
EN
Fuel
2025-01-18
10.2139/ssrn.4963521
preprint
EN
2024-01-01
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